SnpReady for Rice (SR4R) Database

The information commons for rice (IC4R) database is a collection of ∼18 million SNPs (single nucleotide polymorphisms) identified by the resequencing of 5,152 rice accessions. Although IC4R offers ultra-high density rice variation map, these raw SNPs are not readily usable for the public. To satisfy different research utilizations of SNPs for population genetics, evolutionary analysis, association studies and genomic breeding in rice, the raw genotypic data of the 18 million SNPs were processed by unified bioinformatics pipelines. The outcomes were used to develop a daughter database of IC4R – SnpReady for Rice (SR4R). The SR4R presents four reference SNP panels, including 2,097,405 hapmapSNPs after data filtration and genotype imputation, 156,502 tagSNPs selected from linkage disequilibrium (LD)-based redundancy removal, 1,180 fixedSNPs selected from genes exhibiting selective sweep signatures, and 38 barcodeSNPs selected from DNA fingerprinting simulation. SR4R thus offers a highly efficient rice variation map that combines reduced SNP redundancy with extensive data describing the genetic diversity of rice populations. In addition, SR4R provides rice researchers with a web-interface that enables them to browse all four SNP panels, use online toolkits, and retrieve the original data and scripts for a variety of population genetics analyses on local computers. The SR4R is freely available to academic users at http://sr4r.ic4r.org/.

[1]  E. Buckler,et al.  Structure of linkage disequilibrium in plants. , 2003, Annual review of plant biology.

[2]  R. Nielsen.  Molecular signatures of natural selection. , 2005, Annual review of genetics.

[3]  Zhaohui S. Qin,et al.  A second generation human haplotype map of over 3.1 million SNPs , 2007, Nature.

[4]  B. Browning,et al.  Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. , 2007, American journal of human genetics.

[5]  M. Purugganan,et al.  The Extent of Linkage Disequilibrium in Rice (Oryza sativa L.) , 2007, Genetics.

[6]  Bo Wang,et al.  Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection , 2010, Nature Genetics.

[7]  Mark H. Wright,et al.  Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa , 2011, Nature communications.

[8]  Gonçalo R. Abecasis,et al.  The variant call format and VCFtools , 2011, Bioinform..

[9]  Jeffrey B. Endelman,et al.  Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP , 2011 .

[10]  Sudhir Kumar,et al.  MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis , 2012, Bioinform..

[11]  Zhou Du,et al.  PlantGSEA: a gene set enrichment analysis toolkit for plant community , 2013, Nucleic Acids Res..

[12]  Tokurou Shimizu,et al.  Minimal Marker: an Algorithm and Computer Program for the Identification of Minimal Sets of Discriminating DNA Markers for Efficient Variety Identification , 2013, J. Bioinform. Comput. Biol..

[13]  Gad Abraham,et al.  Fast Principal Component Analysis of Large-Scale Genome-Wide Data , 2014 .

[14]  000 rice genomes project The 3.  The 3,000 rice genomes project , 2014 .

[15]  rice genomes.  The 3,000 rice genomes project , 2014, GigaScience.

[16]  D. Akdemir,et al.  Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines , 2015, PLoS genetics.

[17]  Carson C Chow,et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.

[18]  Tanya M. Teslovich,et al.  The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study , 2015, PLoS Genetics.

[19]  Sudhir Kumar,et al.  MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. , 2016, Molecular biology and evolution.

[20]  Open access resources for genome-wide association mapping in rice , 2016, Nature communications.

[21]  Zhang Zhang,et al.  Information Commons for Rice (IC4R) , 2015, Nucleic Acids Res..

[22]  Kenneth L. McNally,et al.  Genomic variation in 3,010 diverse accessions of Asian cultivated rice , 2018, Nature.